System and method for shape based retrieval of prior patients for automation and quality control of radiation therapy treatment plans

ABSTRACT

A system and method for determining at least one new treatment plan for at least one new patient, comprising: providing at least one representation of the at least one new patient&#39;s at least one organ at risk relative to at least one target; searching for at least one prior treatment plan for at least one prior patient with at least one similar representation; and reviewing the at least one prior treatment plan for the at least one prior patient in order to determine whether the at least one new treatment plan can be improved based on information in the at least one prior treatment plan.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.12/820,852, filed Jun. 22, 2010, which is based on and derives thebenefit of the filing date of U.S. Provisional Patent Application No.61/219,458, filed Jun. 23, 2009. All of the foregoing are incorporatedby reference in their entireties.

BRIEF DESCRIPTION OF THE FIGURES

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIGS. 1-2 illustrate a system for determining a therapy treatment plan,according to one embodiment.

FIGS. 3-4 illustrate a method for determining a therapy treatment plan,according to one embodiment.

FIGS. 5-10 illustrate various examples of the system and method,according to several embodiments.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION Shape-BasedRetrieval of Prior Patients Information

In the treatment of patients with malignant tumors, the goal ofintensity-modulated radiation therapy is to deliver a high dose ofradiation to the tumor volume while sparing adjacent organs at risk. Inpractice, a patient is imaged under a computed tomography (CT) scanner,the scan is segmented to identify the primary tumor volume and organs atrisk, and the segmented scan is used by a dosimetrist to determine thebest set of multi-leaf collimator settings to deliver a set of intensitymodulated megavoltage x-ray beams targeting the tumor. Restricted by aset of physician-driven constraints (e.g., at least 95% of the tumorvolume should receive a dosage of at least 70 Gy, no more than 60% ofthe parotid should receive more than 30 Gy, no part of the spinal cordshould receive more than 45 Gy), the dosimetrist uses treatment planningsoftware to optimize the intensity distribution for each of a set ofbeams according to an objective function, derived from the physician'sconstraints. This objective function tells the software how to score thetradeoffs between target coverage and normal tissue sparing. The systemthus finds the set of multi-leaf collimator settings resulting in a dosedistribution that maximizes the dose to the tumor while minimizingharmful radiation to unaffected organs.

When designing the treatment plan, it is helpful to define a shapemetric that captures not only the geometries of the primary tumor volumeand organs at risk (OARs), but also their configurations relative toeach other. For each organ at risk, the distribution of the organ'svolume relative to the primary tumor volume (e.g., the distribution ofdistances of points in an organ from the tumor) can be described. Sincethese distributions encode the distance between the organs at risk andthe tumor volume, and since the spareability of an organ depends on itsproximity to the irradiated tumor, these descriptors can provide asimple shape signature that helps find treatment plans utilized insimilar patients. When similar treatment plans are found, they can bereviewed for helpful information. Once such type of helpful informationis dosimetry information, which is discussed in more detail below.However, any types of information can be utilized from the similartreatment plans. For example, information on side effects (e.g.,toxicities) can be reviewed for prior patients that have similar shapemetrics as the new patient to better understand what side effects and towhat extent the side effects are a risk.

FIG. 1 illustrates a system 100 for determining a radiation therapytreatment plan, according to one embodiment. It should be noted thatother therapy treatment plans can also be determined in alternateembodiments. The system 100 comprises a client computer 115 connected toa server computer 110 over a network 105 (e.g., Internet, intranet). Ashape-based retrieval application 120 can be accessed by the clientcomputer 115 through a server computer 110. Those of ordinary skill inthe art will see that some or all modules of the shape-based retrievalapplication 120 can reside on the server computer, the client computer,or any other computer, or any combination thereof.

FIG. 2 illustrates details related to the shape based retrievalapplication 120, according to one embodiment. Shape based retrievalapplication 120 can comprise: an overlap volume histogram (OVH) module205, a matching module 215, a database 225, a dose volume histogram(DVH) module 210, and an inverse planning module 230. The OVH module 205can measure the shape metrics of the new patient. For example, theOAR(s) proximity to at least one target can be measured. It should benoted that other shape metrics can be measured. The database 225 canhold information related to prior patient treatment plans, including OVHinformation, DVH information, and toxicity information. The matchingmodule 215 can match information from the OVH module 205 to the database225 in order to retrieve records of prior patients whose shape metricsare similar to the new patient. The DVH module 210 can determine the DVHvalue utilizing the OVH information. The inverse planning module 230 canutilize parameters, such as the DVH value, as input parameters forinverse planning utilizing at least one intensity-modulated radiationtherapy (IMRT).

FIG. 3 illustrates a method for determining a radiation therapytreatment plan, according to several embodiments. In 301, data fromprior patients is collected and stored. In some embodiments, thepatients can be identified by the type of cancer.

In 305, a geometric representation of one or more of the new patient'sOARs relative to the target (e.g., tumor, various areas targeted to killvarious stages of cancer) can be provided. It should be noted that insome embodiments, the tumor and all organs at risk can be determinedtogether. In another embodiment, each organ at risk is determinedseparately, and the most relevant patient information is found for eachorgan at risk.

FIG. 4 sets forth details of 305, according to one embodiment. In 405, ashape relationship descriptor, such as, but not limited to, an overlapvolume histogram (OVH) can be determined for one or more of the newpatient's OARs. The OVH can measure the OAR's proximity to a target. Theproximity of the OVH can be distant, proximal, or overlapping withrespect to the target. The OVH can also describe the spatialconfiguration of an organ at risk with respect to a target.

The OVH can be a one-dimensional function giving the volume of an OARthat is within a specific distance r from the target:

$\begin{matrix}{\mspace{85mu} {{{{OVH}(r)} = \frac{\left. {{\text{?}p} \in O} \middle| {{d\left( {p,t} \right)} \leq {r\text{?}}} \right.}{O}}{\text{?}\text{indicates text missing or illegible when filed}}}} & \left( {{EQUATION}\mspace{14mu} 1} \right)\end{matrix}$

In the above Equation 1, O is the OAR, r is the uniform margin distancearound the target T (negative r is contraction, positive r isexpansion), p is any location in the OAR, d (p, T) is the signeddistance of p from the target's boundary (e.g., negative inside theboundary, positive outside the boundary) and 101 is the volume of OAR O.

Thus, all points (e.g., pixels) in an OAR where the distance from thepoint to the target boundary is less than r can be found as the volumeor size of overlap. This is normalized over the total volume or size ofthe OAR. If the target is fully encompassing the OAR, this value is 1.If there is no overlap between the target and the OAR, this value is 0.The OVH can thus represent the percentage of the OAR's volume thatoverlaps with a uniformly expanded or contracted target The calculationof the OVH can be expressed in two steps: contraction and expansion. Incontraction, the target is contracted until there is no overlap betweenthe target and OAR. During contraction, the overlap volume between thecontracted target and OAR is calculated. In expansion, the overlapvolume between the expanded target and OAR is calculated. Expansioncontinues until the target fully encompasses the OAR. The resultingcurve is the OVH that characterizes the relative spatial configurationof the two shapes. Thus, to be able to determine the fullcharacterization of the shape relationship of the OAR to the target, theOVH needs to be calculated from the point where there is no overlapbetween the target and the OAR to the point where the target fullyencompasses the OAR.

For example, FIG. 9 is an example of a patient's differential OVHdescriptors. The image on the left shows the geometry of the tumorvolume (black), the spinal cord (red), the brainstem (blue) and theright and left parotid glands (green). By determining the OVH, theproperties of the geometric configuration of the organs relative to thetumor can be identified. For example, the fact that the OVH of the leftparotid has non-zero values at negative distances can indicate that partof the parotid is overlapped by the tumor volume and therefore it wouldbe difficult to spare the parotid in its entirety. Similarly, since theOVH values for both the spinal-cord and the brainstem are non-zero fordistance values smaller than one centimeter, we know that no point onthe tumor can be within a centimeter of these organs, so a treatmentplan keeping most of the radiation within a centimeter of the tumor islikely to spare them.

As another example, FIG. 5 illustrates a 3-D shape of two OARs and onetarget. The target is represented by a sphere. The OARS are representedby two rectangular boxes with different spatial relationships with thetarget. The integral OVH curves of the two OARs relative to the targetare illustrated in FIG. 5. FIG. 5 shows that the volumes of the two OARS(e.g., OAR₁ and OAR₂) within the target are the same: OVH(0)=35.5%.

The graph of the OVH curves in FIG. 5 shows that the non-overlappingportion of OAR₂ (r>0) is more slowly encompassed by the target (e.g.,the OVH shoots up to 1 more slowly) as the target area is expanded, ascompared with the non-overlapping portion of OAR₁ (e.g., the OVH shootsup to 1 faster). Accordingly, the non-overlapping portion of OAR₂ ismore easily spared that than of OAR₁. However, the OVH curve of OAR₁ ismuch steeper when r<0. This indicates that the overlapping portion ofOAR₁is more easily spared than that of OAR₂. This can be important, forexample, when planning if and how to apply doses to OARS. For example,if an OAR is a serial OAR (e.g., spinal cord), if any portion of the OARgets a high dose, it can causes serious problems. In contrast, if an OARis a parallel OAR (e.g., lung), half of the lung can receive a high doesand the lung can still function properly. Thus, even though various OARsmay have similar shapes, the OVH relationship of OARs to targets canhelp determine spareability. FIG. 7 illustrates how the OVH can map theshape of an OAR to a volume distance plane by expanding and contractingthe target. The yellow represents expansion of the target. The redrepresents contraction of the target.

Referring back to FIG. 4, in 410, the dose volume histogram (DVH) can bedetermined. It should be noted that in a conformal (e.g., 3-D) dosedistribution, the DVH of an OAR can be directly related to the OVH ofthat OAR. A conformal dose distribution can be defined by the followingproperties: 1) the target's boundary is covered by the iso-dose surface(e.g., designating points representing equal points of radiation) ofprescription dose D_(p); 2) any iso-dose surface is an expansion orcontraction of the target's boundary; 3) the dose decays monotonically(e.g., consistently decreasing) with distance away from the target'sboundary; and 4) the dose grows monotonically (e.g., consistentlyincreasing) with distance inside the target's boundary. In thisconformal dose distribution, the larger the expansion distance r_(v) atvolume v (i.e., OVH (r_(v))=v), the easier the OAR is to spare (i.e.,the lower the D_(v) is). The D_(v) represents the DVH dose at volume v(i.e., DVD (D_(v))=v). This property makes it possible to compare theDVHs of OAR₁ and OAR₂ based on their OVHS:

r _(v,1) ≧r _(v,2)

D _(v,1) ≦D _(v,2)  (EQUATION 2)

In the above Equation 2, r_(v,1) is the expansion distance for OAR₁;r_(v,2) is the expansion distance for OAR₂; D_(v,1) is the prescriptiondose for OAR₁ and D_(v,2) is the prescription dose for OAR₂. Forexample, applying Equation 2 to the OVH curves in FIG. 5 leads to thefollowing conclusions: for v>OVH(0), we have D_(v,1)>D_(v,2), forv<OVH(0), we have D_(v,1)<D_(v,2); and for v=OVH(0), we haveD_(v,1)=D_(v,2)

The logic of Equation 2 can thus indicate that if patient 1 had a largerdistance from the OAR to the target than patient 2, a lesser dose shouldbe used for patient 1 as opposed to patient 2. Thus, if the new patientis set to be patient 1, all the patients that should be harder to plan(e.g., that have OARs closer to the target area and thus should requirelarger doses). Then, the patient that utilized the least amount ofdosage successfully can be found.

As an alternative, a search can be done for patients that have OVHssimilar to the new patients. For example, all patients whose OVHdistance(s) of the OAR(s) to the target is close to (e.g., within Xamount) the OVH distance(s) of OAR(s) to the target in the new patient,can be found. Then, the patient that utilized the least amount of dosagesuccessfully can be found.

It should be noted that a conformal dose distribution may not bepractically achievable due to the irregular shape of targets, the needto spare the OARs, and the inhomogenous densities of patient tissues.However, Equation 2 can still be used to relate the OVH and DVH innon-conformal dose distribution, in, for example, but not limited to,the following circumstances: 1) where planners spend time making theprescription dose conformal to the target; 2) where the target's DVHdose is at 95% volume, D₉₅, and D₉₅ must be larger than prescriptiondose D_(p), (i.e., D₉₅>D_(p)); or 3) where the densities of the targetand its surrounding soft tissue are similar; or any combination thereof(Those of ordinary skill in the art will see that other circumstancesare possible where Equation 2 can be used.) As a result, Equation 2 canbe used to approximate conformal dose distribution around the target

Referring back to FIG. 3, in 306, an initial plan for a new patient canbe determined based on the geometric representation.

In 310, the new patients' geometric representation can be used to searchthe database 225 for prior patients with similar geometricrepresentations. In one embodiment, the DVH and the OVH for the newpatient (a query OAR_(q)) can be used to query the database for priorpatient(s) with similar values for OVH and DVH. For each patient, thedatabase can store the DVHs of the OARs, the DVHs of the targets, andthe OVHs of the OARs. The query can return the set {i} of plans forprior patients that satisfy the following conditions for the volume v ofthat OAR's planning goal:

{i: r_(v,q)≧r_(v,i) and D_(v,q)≦D_(v,i)}  (EQUATION 3)

In the above Equation 3, r_(v,q) is the expansion distance for OAR_(q);r_(v,i) is the expansion distance for OAR_(i); D_(v,q) is theprescription dose for OAR_(q) and D_(v,i) is the prescription dose forOAR_(i) If at least one prior patient's plan meets both conditions ofEquation 3, it may be possible to deliver a lower dose to the query OAR,because that prior patient's plan did so. In this case, re-planning maybe necessary, as explained in 315 of FIG. 3, to reduce the dose of thequery OAR.

Thus, as set forth above, the geometric relationship between thetarget(s) and OAR(s) of a new patient can be compared with the geometricrelationships between the target(s) and OAR(s) of prior patients, whoseplans are maintained in database 225. By comparing the geometricconfigurations, planners can identify those prior patients who havegeometric configurations similar to those of the new patient. Thetreatment plans of the similar prior patients can be retrieved fromdatabase 225 and used to guide planners in determining whether at leastone dose in at least one new plan for the new patient is appropriate.For example, in one embodiment, a new plan can be compared to an oldplan to determine whether a lower dose to an OAR in the new plan can beused. Database 225 can thus serve the function of multiple planners butcan claim fewer resources and can offer more efficiency. In addition,the database can enhance experience sharing among planners, such as, butnot limited to, inexperienced planners.

Referring back to FIG. 3, in 315, the most relevant patient information(from one or more patients) can be used to revise the new patient'streatment plan. In one embodiment, an intensity modulated radiationtherapy (IMRT) plan can be determined and inverse planning can be doneto come up with a revised treatment plan for the new patient. Moreinformation on how inverse planning can be determined using, forexample, an IMRT, is found in Thomas Bortfeld et al., IMAGE-GUIDED IMRT(2006) and I. J. Das. et al., “Intensity-Modulated Radiation TherapyDose Prescription, Recording, and Delivery: Patterns of VariabilityAmong Institutions and Treatment Planning Systems”, J. Natl. CancerInst., 100:5, pp. 300-307 (2008), both of which are herein incorporatedby reference.

For example, after a treatment plan is designed, a simulation of theIMRT can be performed to determine the resulting dose distribution. Thequality of the treatment plan can then be evaluated by utilizing theDVHs of the current plan and previous plans. FIG. 10 illustrates anexample of the DVHs derived from the treatment plan for the patientshown in FIG. 9. Since the goal of the treatment is to destroy thetumor, the treatment plan results in a DVH for the tumor that has largevalues for all doses. For serial organs like the spinal-cord andbrain-stem, the goal of the treatment can be to ensure that no part ofthe organ receives a high dose, and the DVHs for both have zero valuebeyond 50 Gy. Since the parotids are parallel organs, they remainfunctional even after a noticeable fraction of their volume has receiveda high dose, the DVHs for both the left and right parotids show smallvolumes of the organ receiving doses larger than 55 Gy. Additionally,since the proximity of the left parotid gland to the tumor make it hardto spare, the treatment results in more irradiation of this gland, with10% of the organ receiving as much as 70 Gy.

The quality of a shape based descriptor can thus be measured bydetermining the effectiveness with which it retrieves patients havingsimilar DVHs. This can be done by calculating the variation in DVHdistances between a patient and the patient's k nearest neighbors(sorted by descriptor similarity).

For example, given a new patient, the sum of squared distances from theDVH of the new patient to the DVH of the patient's nearest neighbors canbe computed and summed over both the nearest neighbors and the differentorgans at risk. This can be done for all patients in the database andthe sums can be averaged. This can give a 1D distribution of theexpected distance of a patient's DVH from the DVH of its k nearestneighbors. Descriptors that better predict DVH similarity can give riseto distributions with smaller expected distances. The best results canthus often be obtained when patients are sorted based on DVH similarity.

EXAMPLES

For example, the parotid DVHs of 32 prior head-and-neck patients can besearched. Each parotid DVH can be queried against the other parotid DVHsto determine whether a lower dose is possible. In one example, 17parotid DVHs can be flagged as promising the greatest reduction in D₅₀(the DVH does at 50% volume). These 17 parotids can be determined tocome from 13 patients. Then, the patient that used the least amount ofradiation is determined to be the patient with the most relevant patientinformation. This most relevant patient information can guide plannersin determining whether lower doses delivered to the organs at risk arefeasible. In another embodiment, the prior patient with the closest OVHcan be determined to be the most relevant prior patient.

A more detailed example follows: Patients with head and neck cancer canbe treated by dose painting (an IMRT technique), which can deliver threedifferent prescription doses to the electively irradiated nodal regionsand the gross disease sites. In one example, for each patient, threetargets can be set: PTV^(L) (low prescription planning targetvolume)=58.1 Gy; PTV^(M) (medium prescription planning target volume)=63Gy; and PTV^(H) (high prescription planning target volume)=70 Gy.Correspondingly, each OAR has three OVHs, corresponding to each of thethree PTVs.

FIG. 6 shows various 3D geometric relationships of the parotid glands oftwo patients with respect to their three PTVs. The red represents theright parotid gland, the yellow represents the left parotid gland, andthe green represents the target. The OVHs can be found for the right andleft parotids with respect to the three PTV targets. The DVHs can thenbe predicted. The database of prior patients can then be searched forsimilar DVHs.

The OVH curves depicting the geometric relationships of the rightparotids of two patients with respect to their three PTVs are shown inFIG. 8. The OVH curves illustrate that the distances between the leftparotid of patient 1 (1L) and its three PTVs are larger than thedistances between the left parotid of patient 2 (5 L) and its three PTVsfor any volume v. FIG. 8 also indicates that 2 L is closer to its PTV⁷⁰,since the OVH⁷⁰ curve of 2 L is on the left of the OVH⁷⁰ curve of 2 Lfor any v.

Applying Equation 2 from above may lead to the conclusion that 1 Lshould receive a lower dose than 2 L for any v. However, the DVH curvesof the left parotids in FIG. 8 show the opposite. This discrepancyindicates that the dose of 1 L can be further reduced, and re-planningfor patient 1 may be needed. The re-planning results of patient 1 aredetailed below.

CONCLUSION

While various embodiments have been described above, it should beunderstood that they have been presented by way of example, and notlimitation. It will be apparent to persons skilled in the relevantart(s) that various changes in form and detail can be made thereinwithout departing from the spirit and scope. In fact, after reading theabove description, it will be apparent to one skilled in the relevantart(s) how to implement alternative embodiments. Thus, the presentembodiments should not be limited by any of the above-describedembodiments

In addition, it should be understood that any figures which highlightthe functionality and advantages, are presented for example purposesonly. The disclosed methodology and system are each sufficientlyflexible and configurable, such that it may be utilized in ways otherthan that shown. For example, the steps listed in any flowchart may bere-ordered or only optionally used (even when not explicitly indicated)in some embodiments. Thus, those skilled in the art will realize thatthe ordering of the steps in FIGS. 3-7 can be altered in otherembodiments and that various steps can be removed in some embodiments.

Further, the purpose of the Abstract of the Disclosure is to enable theU.S. Patent and Trademark Office and the public generally, andespecially the scientists, engineers and practitioners in the art whoare not familiar with patent or legal terms or phraseology, to determinequickly from a cursory inspection the nature and essence of thetechnical disclosure of the application. The Abstract of the Disclosureis not intended to be limiting as to the scope of the present inventionin any way.

Finally, it is the applicant's intent that only claims that include theexpress language “means for” or “step for” be interpreted under 35U.S.C. 112, paragraph 6. Claims that do not expressly include the phrase“means for” or “step for” are not to be interpreted under 35 U.S.C. 112,paragraph 6.

1. A method for determining at least one similar treatment plan,comprising: providing, utilizing at least one overlap volume histogram(OVH) module on at least one computer, at least one representation of atleast one new patient's at least one organ at risk relative to at leastone target; determining, utilizing at least one matching module on theat least one computer, at least one prior treatment plan for at leastone prior patient with at least one similar representation.